CHART
Chart by Gapminder
It’s not the numbers that are interesting. It’s what they tell us about the lives behind the numbers…
— Hans Rosling
This epic chart was presented by Hans Rosling to illustrate that income and health go hand in hand. People live longer in richer countries. Or the other way around. Countries are richer where people live longer. There are no high income countries with a short life expectancy, and no low income countries with a long life expectancy. Still, there’s a huge difference in life expectancy between countries on the same income level, depending on how the money is distributed and how it is used.
The y-axis shows a continium from healthy to sick, the x-axis from rich to poor. The bubbles are sized by population and colored by continent.
url_root <- "https://raw.githubusercontent.com/UN-AVT/kamino-source/main/sources/0-shared/data/"
url_file <- "gap-minder/gapminder.csv"
url <- paste0(url_root, url_file)
gapminder <- read.csv(url, header = TRUE, stringsAsFactors = FALSE)
gapmindergapminder$continent <- countrycode(gapminder$country_code, origin = 'iso3c', destination = 'continent')
gapmindertheme_opts <- theme(
text = element_text(family = "inconsolata"),
plot.title = element_text(color = "black", size = 14, face = "bold"),
plot.subtitle = element_text(color = "black", size = 12),
plot.caption = element_text(color = "#555555", size = 8),
# axis.title.x = element_blank(),
# axis.title.y = element_blank(),
# axis.text.x = element_text(vjust = 12),
axis.line.x = element_line(color="black", size = 1),
axis.line.y = element_line(color="black", size = 1),
panel.border = element_blank(),
panel.background = element_blank(),
panel.grid.minor = element_blank(), # remove minor gridlines
# panel.grid.major.x = element_blank(), # remove x (vertical) gridlines
# panel.grid.major.y = element_blank(), # remove y (horizontal) gridlines
legend.title = element_blank(), # remove legend title
# legend.text = element_text(color = "black", size = 8),
legend.position='none'
)
v1 <- ggplot(gapminder, aes(x = gdp, y = life_expectancy)) +
# Coordinate parameters
ylim(50, 90) +
scale_x_log10(limits = range(gapminder$gdp)) +
ggtitle("World Health Chart 2019 by Gapminder") +
theme_bw() +
theme_opts
girafe(ggobj = v1, width_svg = 16, height_svg = 9,
options = list(opts_sizing(rescale = TRUE, width = 1.0)))v2 <- ggplot(gapminder, aes(x = gdp, y = life_expectancy)) +
# Shape visual variable
geom_point() + ylim(50, 90) +
scale_x_log10(limits = range(gapminder$gdp)) +
ggtitle("World Health Chart 2019 by Gapminder") +
theme_bw() +
theme_opts
girafe(ggobj = v2, width_svg = 16, height_svg = 9,
options = list(opts_sizing(rescale = TRUE, width = 1.0)))v3 <- ggplot(gapminder, aes(x = gdp, y = life_expectancy, size = pop)) +
geom_point() +
ylim(50, 90) +
# Size visual variable
scale_size("population", range = c(2, 40)) +
scale_x_log10(limits = range(gapminder$gdp)) +
ggtitle("World Health Chart 2019 by Gapminder") +
theme_bw() +
theme_opts
girafe(ggobj = v3, width_svg = 1280/72, height_svg = 720/72,
options = list(opts_sizing(rescale = TRUE, width = 1.0)))# Manual palette to match colors
continent_palette <- c("Africa" = "#77dff7", "Americas" = "#b5ea32", "Asia" = "#ff5973", "Europe" = "#ffe800", "Oceania" = "#ff5973")
v4 <- ggplot(gapminder, aes(x = gdp, y = life_expectancy, size = pop, fill = continent)) +
geom_point(shape = 21, color = "#ffffff") + ylim(50, 90) +
scale_size("population", range = c(2, 40)) +
scale_x_log10(limits = range(gapminder$gdp)) +
# Color mapping
scale_fill_manual(values = continent_palette) +
ggtitle("World Health Chart 2019 by Gapminder") +
theme_bw() +
theme_opts
girafe(ggobj = v4, width_svg = 16, height_svg = 9,
options = list(opts_sizing(rescale = TRUE, width = 1.0)))v5 <- ggplot(gapminder, aes(x = gdp, y = life_expectancy, fill = continent, label = country )) +
geom_point(aes(size = pop), shape = 21, color = "#ffffff") +
# Label mapping
# geom_text() +
# geom_text(hjust = 1, size = 2) +
# geom_text_repel(aes(size = pop/2)) +
geom_text_repel(data=filter(gapminder, pop<mean(pop)), aes(size = pop/2), family = "inconsolata") +
scale_size("population", range = c(2, 40)) +
scale_x_log10(limits = range(gapminder$gdp)) +
ylim(50, 90) +
# Color mapping
scale_fill_manual(values = continent_palette) +
ggtitle("World Health Chart 2019 by Gapminder") +
theme_bw() +
theme_opts
girafe(ggobj = v5, width_svg = 16, height_svg = 9,
options = list(opts_sizing(rescale = TRUE, width = 1.0)))gapminder <- gapminder[order(gapminder$pop, decreasing = TRUE),]
v6 <- ggplot(gapminder, aes(x = gdp, y = life_expectancy, fill = continent, label = country )) +
# Shape mapping
geom_point(aes(size = pop), shape = 21, color = "#ffffff") +
# Label mapping
geom_text_repel(data=filter(gapminder, pop<mean(pop)), aes(size = pop/2), family = "inconsolata") +
# Size mapping
scale_size("population", range = c(2, 40)) +
# Location mapping
scale_x_log10(limits = range(gapminder$gdp), breaks=c(1000,2000,4000,8000,16000,32000,64000)) +
scale_y_continuous(limits = c(50, 90), breaks=c(55,60,65,70,75,80,85)) +
# Color mapping
scale_fill_manual(values = continent_palette) +
ggtitle("World Health Chart 2019 by Gapminder") +
ylab("Lifespan, Life Expectancy") +
xlab("Income, GDP per Capita") +
theme_bw() +
theme_opts
girafe(ggobj = v6, width_svg = 16, height_svg = 9,
options = list(opts_sizing(rescale = TRUE, width = 1.0)))